Endorsing local search results

ABSTRACT

Methods and systems for improving user search experience with a search engine by providing a way for associated users to create and share personalized lists of local search results and/or advertisements through endorsements of such local search results and/or ads. Local search endorsements can be used to personalize the search engine&#39;s ranking of local search results by offering a way for users to re-rank the results for themselves and for those who trust them.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/308,974, filed Jun. 19, 2014, which is a continuation of U.S.application Ser. No. 13/618,042, filed Sep. 14, 2012, which is acontinuation of U.S. application Ser. No. 12/912,037, filed Oct. 26,2010, which is a continuation of U.S. application Ser. No. 10/879,591,filed Jun. 30, 2004, which is related to U.S. application Ser. No.10/879,592, filed Jun. 30, 2004. The contents of each are herebyincorporated by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates generally to methods and systems forsearching. For example, embodiments of the present invention relategenerally to methods and systems for using member networks to improve auser's search experience with a search engine.

Background

A conventional search engine, such as the Google™ search engine, returnsa result set in response to a search query submitted by a user. Thesearch engine performs the search based on a conventional search method.For example, one known method, described in an article entitled “TheAnatomy of a Large-Scale Hypertextual Search Engine,” by Sergey Brin andLawrence Page, assigns a degree of importance to a document, such as aweb page, based on the link structure of the web page. The search engineranks or sorts the individual articles or documents in the result setbased on a variety of measures. For example, the search engine may rankthe results based on a popularity score. The search engine generallyplaces the most popular results at the beginning of the result set. Someconventional search engines also include electronic yellow pages toprovide searches of individual product/service providers (e.g.,restaurants, tax services, auto repair services, etc.) in a particularlocality. Such local searches enable users to locate desiredproduct/service providers that do not ordinarily appear in regularsearches because they do not have their own websites or URLs.

Conventional websites (also written as “Web sites”) such as those hostedon Yahoo!™ Tribe™, Tickle™, or other web sites, allow users to formcommunities, groups, and/or other member networks. The member networkson conventional websites allow members of the group to communicate witheach other and list announcements associated with the community.Generally, conventional web sites do not connect the member networkswith search engines and enable members of such networks to endorse orrecommend search results, particularly online advertisements and/orsearch results of local individual product/service providers, to oneanother.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide methods and systems formembers of a member networks to endorse or recommend to other members orusers local articles and/or advertisements (hereinafter, “ads”) forparticular search queries. In one embodiment of the present invention,there is provided a method comprising: receiving endorsement informationof endorsed local articles or ads; receiving a local search query; andproviding a search result set relevant to the local search query,wherein the search result set includes at least one endorsed articleidentifier for one of the endorsed local articles or ads.

In another embodiment of the present invention, there is provided amethod comprising: receiving a first user profile in a member networkcreated by a first user; receiving a first local search query submittedby the first user; returning from a search engine to the first user alocal search result set relevant to the first local search query, thelocal search result set includes one or more links for endorsing localarticles identified in the search result set; receiving from the firstuser a first endorsement for one of the local articles identified in thelocal search result set; storing the first endorsement for the localarticle in a member network database; receiving a second user profile inthe member network created by a second user; receiving a second localsearch query submitted by the second user that is substantiallyidentical or relevant to the first local search query; returning fromthe search engine a second local search result set relevant to thesecond local search query; returning from the member network database athird local search result set relevant to the second search query; andmerging the second local search result set with the third local searchresult set to provide the second user with a final local search resultset identifying the first endorsement for the second local search query.

The aforementioned embodiments are mentioned not to limit or define theinvention, but to provide an example of embodiments of the invention toaid understanding thereof. Such exemplary embodiments are discussed inthe Detailed Description, and further description of the invention isprovided there. Advantages offered by the various embodiments of thepresent invention may be further understood by examining thisspecification.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present invention are illustrated by way ofexample in, and not limited to, the following figures:

FIG. 1 is a block diagram illustrating an exemplary environment in whichembodiments of the present invention may operate;

FIG. 2 depicts a diagram of a member network in accordance with anembodiment of the present invention; and

FIGS. 3A-B depict process flows for local search endorsements inaccordance with an embodiment of the present invention.

FIG. 4 depicts a sample screenshot of a local articles page inaccordance with an embodiment of the present invention;

FIG. 5 depicts a sample screenshot of an endorsement page in accordancewith an embodiment of the present invention; and

FIGS. 6A-B depict process flows for local search endorsements inaccordance with another embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION Overview

Embodiments of the present invention provide methods and systems formembers of a member networks to endorse or recommend to other members orusers a localized article or articles, which may include individualproduct/service providers and/or ads for a desired locality. In oneembodiment, the method begins with a user signing up to become a memberof a member network. After becoming a member, the user can endorse orrecommend a local article(s) or ad(s). When an endorsed articleidentifier appears in a result set relevant to a local search query, thearticle identifier can have associated endorsement data. Further, as amember, the user can submit local user queries to the search engine andreceive lists of search results that include article identifiers ofendorsed articles and/or ads from other members of the member network.Each list of article identifiers in a local search result set can bere-ordered or re-ranked to reflect those endorsed article identifierswithin the list. Thus, the search endorsements can be used to improvethe search engine's ranking of local search results and endorsed ads byoffering a way for users to re-rank the local search results andendorsed ads for themselves and for those who trust them.

System Architecture

Various systems in accordance with the present invention may beconstructed. FIG. 1 is a block diagram illustrating an exemplary systemin which embodiments of the present invention can operate. The presentinvention may operate, and be embodied, in other systems as well.

Referring now to the drawings in which like numerals indicate likeelements throughout the several figures, FIG. 1 is a block diagramillustrating an exemplary system in accordance with an exemplaryembodiment of the present invention. The system 100 shown in FIG. 1includes multiple client devices 102 a-n with users 112 a-112 n incommunication with a search site 150 and a member network site 160 overa network 106. The search site 150 and the member network site 160 arealso in communication with each other directly (as shown by the dashedline) or through the network 106. The network 106 can be a wired orwireless network. Further, it can be a public network, e.g., theInternet, or a private data network, e.g., a local area network (LAN) ora wide area network (WAN). Moreover, methods according to the presentinvention may operate within a single computer.

Each of the client devices 102 a-n includes a memory 108, which can be acomputer-readable medium (CRM), such as a random access memory (RAM),coupled to a processor 110. The processor 110 executescomputer-executable program instructions stored in the client device,such as memory 108, as program code. Such processor may include amicroprocessor, an ASIC, and state machines. Such processors include, ormay be in communication with, media, for example computer-readablemedia, which stores instructions that, when executed by the processor,cause the processor to perform the methods described herein. Moreover,the processor 110 can be any of a number of computer processors, such asprocessors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Ill. Embodiments of computer-readable mediainclude, but are not limited to, an electronic, optical, magnetic, orother storage or transmission device capable of providing a processor,such as the processor 110 of client 102 a, with computer-readableinstructions. Other examples of suitable media include, but are notlimited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM,RAM, an ASIC, a configured processor, all optical media, all magnetictape or other magnetic media, or any other medium from which a computerprocessor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, switch, private or public network, orother transmission device or channel, both wired and wireless. Theinstructions may include code from any suitable computer-programminglanguage, including, for example, C, C++, C#, Visual Basic, Java,Python, Perl, and JavaScript.

Client devices 102 a-n can also include a number of external or internaldevices such as a mouse, a CD-ROM drive, a DVD drive, a keyboard, adisplay, or other input or output devices. Examples of client devices102 a-n are personal computers, digital assistants, personal digitalassistants (PDAs), cellular phones, mobile phones, smart phones, pagers,digital tablets, laptop computers, Internet appliances, and otherprocessor-based devices. In general, the client devices 102 a-n can beany type of processor-based platform that operates on any operatingsystem capable of supporting one or more client application programs.Client devices 102 a-n may operate on any operating system capable ofsupporting a browser or browser-enabled application, such as Microsoft®Windows® or Linux. The client devices 102 a-n shown include, forexample, personal computers executing a browser application program suchas Microsoft Corporation's Internet Explorer™, Netscape CommunicationCorporation's Netscape Navigator™, and Apple Computer, Inc.'s Safari™.

Through the client devices 102 a-n, users 112 a-n can communicate overthe network 106 with each other and with other sites, systems anddevices coupled to the network 106. As shown in FIG. 1, a search site150 and a member network site 160 are also coupled to the network 106.

The search site 150 shown includes a server device 152 executing asearch application program, also known as a member network engine 168.The member network engine 168 allows users, such as user 112 a, tointeract with and participate in a member network. A member network canrefer to a computer network connecting entities, such as people ororganizations, by a set of social relationships, such as friendship,co-working, or information exchange. Of course, a member network canrefer to a computer application or data connecting such entities by suchsocial relationships. Examples of member networks include Orkut.com andFriendster.com.

Member networks can comprise any of a variety of suitable arrangements.An entity or member of a member network can have a profile and thatprofile can represent the member in the member network. The membernetwork can facilitate interaction between member profiles and allowassociations or relationships between member profiles. Associationsbetween member profiles can he one or more of a variety of types, suchas friend, co-worker, family member, business associate, common-interestassociation, and common-geography association. Associations can alsoinclude intermediary relationships, such as friend of a friend, anddegree of separation relationships, such as three degrees away.

Associations between member profiles can be reciprocal associations. Forexample, a first member can invite another member to become associatedwith the first member and the other member can accept or reject theinvitation. A member can also categorize or weigh the association withother member profiles, such as, for example, by assigning a level to theassociation. For example, for a friendship-type association, the membercan assign a level, such as acquaintance, friend, good friend, and bestfriend, to the associations between the member's profile and othermember profiles. In one embodiment, the member network engine 168 candetermine the type of association between member profiles, including, insome embodiments, the degree of separation of the association and thecorresponding weight or level of the association.

Similar to the client devices 102 a-n, the server device 152 shownincludes a processor 154 coupled to a CRM 156. Server device 152,depicted as a single computer system, may he implemented as a network ofcomputer processors. Examples of the server device 162 are servers,mainframe computers, networked computers, a processor-based device, andsimilar types of systems and devices. The server processor 154 can beany of a number of computer processors, such as processors from IntelCorporation of Santa Clara, Calif. and Motorola Corporation ofSchaumburg, Ill.

Memory 156 contains a search application program, also known as a searchengine 158. The search engine 158 locates relevant information inresponse to a search query from one of the client devices 102 a-n, e.g.,the client device 102 a. In the embodiment shown, the server device 152,or related devices, has previously performed a crawl of the network 106to locate articles, such as web pages, stored at other devices orsystems coupled to the network 106, and indexed the articles in anarticle index for storage in memory 156 or another data storage device.Thus, the search engine 158 can locate relevant information by accessingthe article index in response to a search query. The search engine 158then provides a result set to the client device 102 a via the network106. The result set comprises one or more identifiers of articles thatare relevant to the search query. Articles include, for example: wordprocessor, spreadsheet, presentation, e-mail, instant messenger,database, and other client application program content files or groupsof files; web pages of various formats (e.g., HTML, XML, XHTML);portable document format (PDF) files; audio tiles; video files; or anyother documents or groups of documents or information of any typewhatsoever. An article identifier may be, for example, a uniformresource locator (URL), a uniform resource identifier (URI), a filename, a link, an icon, a path for a local file, or anything else thatidentifies an article or ad.

The member network site 160 shown includes a member network database 170and a server device 162 executing a member network engine applicationprogram. Similar to the client devices 102 a-n, the server device 162shown includes a processor 164 coupled to a CRM 166. The server device162 is in communication with a member network database 170. Serverdevice 162, depicted as a single computer system, may be implemented asa network of computer processors. Examples of the server device 162 areservers, mainframe computers, networked computers, a processor-baseddevice, and similar types of systems and devices. The server processor164 can be any of a number of suitable computer processors, such asprocessors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Ill.

Memory 166 in the server device 162 contains a member network engineapplication program, also known as a member network engine 168. Themember network engine 168 allows users, such as user 112 a, to interactwith and participate in a member network. A member network can refer toa computer network connecting people or organization by a set ofrelationships, such as social relationships like friendship, co-working,or information exchange. A member network can include profiles that canbe associated with other profiles. Each profile may represent a memberand a member can be, for example, a person, an organization, a business,a corporation, a community, a fictitious person, or other entity. Eachprofile can contain entries, and each entry can include informationassociated with a profile. Examples of entries for a person profile caninclude information regarding relationship status, birth date, age,children, ethnicity, religion, political view, sense of humor, sexualorientation, fashion preferences, smoking habits, drinking habits, pets,hometown location, passions, sports, activities, favorite books, music,TV, or movie preferences, favorite cuisines, email addresses, locationinformation, IM name, phone number, address, skills, career, or anyother information describing, identifying, or otherwise associated witha profile. Entries for a business profile can include market sector,customer base, location, supplier information, net profits, net worth,number of employees, stock performance, or other types of informationassociated with the business profile.

Additionally, entries within a profile can include associations withother profiles. Associations between profiles within a member networkcan include, for example, friendships, business relationships,acquaintances, community or group associations, activity partnerassociations, common interest associations, common characteristicassociations, or any other suitable type of relationship connection(e.g., social relationship connection). Associations between profilescan also have various levels. For example, friendship levels caninclude, for example, a “haven't met” level, an “acquaintance” level, a“friend” level, a “good friend” level, a “best friend” level, and othersuitable levels.

A degree of separation based on associations between profiles can alsobe determined. For example, a degree of separation can be determinedbased on the fewest number of associations between two profiles. Thus,if profile A is a friend of profile B, and profile B is a friend ofprofile C, there can be a degree of separation of two between profiles Aand C. A degree of separation can be type specific or type neutral. Typespecific degrees of separation only count relationships of a certaintype. Thus, for example, in the case above where A is a friend of B, andB is a friend of C, there is a friendship degree separation of two, evenif A is directly associated with C by a business association, whichwould otherwise produce a degree of separation of 1.

Moreover, each profile can also contain local-search endorsemententries, each entry can include information associated with an endorsedlocal article. For example, a local-search endorsement entry can includea particular local search query, one or more article identifiers forlocal articles and/or ads that the user has endorsed for the localsearch query, and the kind of endorsement for each of the endorsed localendorsed articles and/or ads.

Server device 162 of the member network site 160 also provides access tostorage elements, such as a member network storage element, in theexample shown in FIG. 1, a member network database 170. The membernetwork database 170 can be used to store profiles of members in amember network and to store communities within the member network ascreated by the member-network engine 168. Data storage elements mayinclude any one or combination of methods for storing data, includingwithout limitation, arrays, hash tables, lists, and pairs. Other similartypes of data storage devices can be accessed by the server device 162.The member network engine 168 can receive data comprising the profilesand communities from the member-network database 170 and can also senddata comprising communities and profiles to the member network database170 for storage. The member-network database 170 may be physicallyattached or otherwise in communication with the member-network engine168 by way of a network or other connection.

In operation, upon receiving a search query from a user, such as one ofthe users 112 a-n, the search engine 158 locates relevant information inresponse to the search query. The search engine 158 then returns a firstresult set of one or more article identifiers relevant to the searchquery. The search engine 158 also communicates with the member networkengine 168 to access the member network database 170, look uplocal-search endorsement entries in member profiles that are associatedwith the user in a member network as further explained later, and returna second result set of one or more endorsed local article identifiers.The two search result sets are then merged to provide the user with afinal search result set having article identifiers for local articlesand/or ads that are relevant to the search queries, with some articleidentifiers indicated as having been endorsed based on the second searchresult set. The final search result set also provides the user with anoption to endorse one or more local articles and/or ads in the memberprofile. The methods for accomplishing these tasks are described belowin the process section.

It should be noted that the present invention may include systems havingdifferent architecture than that which is shown in FIG. 1. For example,in some systems according to the present invention, server device 162may include a single physical or logical server. The system 100 shown inFIG. 1 is merely exemplary, and is used to help explain the membernetworks and methods illustrated in subsequent figures.

Exemplary Member Network

FIG. 2 shows a diagram of a member network 200 according to oneembodiment of the present invention. According to the embodimentillustrated in FIG. 2, the member network 200 is illustrated with agraph comprising vertices 202, 204, 206, 208, 210, 212, and 214 andedges 218, 220, 222, 224, 226, 228, 230, 232, and 234. The vertices 202,204, 206, 208, 210, 212, and 214 comprise profiles A, B, C, D, E, F, andG, respectively. Each profile can represent a member profile of a memberof the member network 200. The exemplary network 200 shown in FIG. 2 hasseven members. Considerably more members can be part of the membernetwork 200. A member can be an entity such as, for example, a person,an organization, a business, a corporation, a community, a fictitiousperson, or other suitable entity.

Each member profile can contain entries, and each entry can compriseinformation associated with a profile. For example, a person's memberprofile can contain: personal information, such as relationship status,birth date, age, children, ethnicity, religion, political view, sense ofhumor, sexual orientation, fashion preferences, smoking habits, drinkinghabits, pets, hometown location, passions, sports, activities, favoritebooks or music, TV or movie preferences, and favorite cuisines; contactinformation, such as email addresses, location information, instantmessenger name, telephone numbers, and address; professionalinformation, such as job title, employer, and skills; educationalinformation, such as schools attended and degrees obtained, and anyother suitable information describing, identifying, or otherwiseassociated with a person. A business' member profile can, for example,contain a description of the business, and information about its marketsector, customer base, location, suppliers, net profits, net worth,number of employees, stock performance, contact information, and othertypes of suitable information associated with the business.

A member profile can also contain rating information associated with themember. For example, the member can be rated or scored by other membersof the member network 200 in specific categories, such as humor,intelligence, fashion, trustworthiness, sexiness, and coolness. Amember's category ratings can be contained in the member's profile. Inone embodiment of the member network, a member can have fans. Fans canbe other members who have indicated that they are “fans” of the member.Rating information can also include the number of fans of a member andidentifiers of the fans. Rating information can also include the rate atwhich a member accumulated ratings or fans and how recently the memberhas been rated or acquired fans.

A member profile can also contain membership information associated withthe member. Membership information can include information about amember's login patterns to the member network, such as the frequencythat the member logs in to the member network and the member's mostrecent login to the member network. Membership information can alsoinclude information about the rate and frequency that a member profilegains associations to other member profiles. in a member network thatcomprises advertising or sponsorship, a member profile may containconsumer information. Consumer information may include the frequency,patterns, types, or number of purchases the member makes, or informationabout which advertisers or sponsors the member has accessed, patronized,or used.

A member profile may comprise data stored in memory. The profile, inaddition to comprising data about the member, can also comprise datarelating to others. For example, a member profile can contain anidentification of associations or virtual links with other memberprofiles. In one embodiment, a member's member network member profilemay comprise a hyperlink associated with another member's profile. Inone such association, the other member's profile may contain areciprocal hyperlink associated with the first member's profile. Amember's profile may also contain information excerpted from anotherassociated member's profile, such as a thumbnail image of the associatedmember, his or her age, marital status, and location, as well as anindication of the number of members with which the associated member isassociated. In one embodiment, a member's profile may comprise a list ofother member network members' profiles with which the member wishes tobe associated.

An association may be designated manually or automatically. For example,a member may designate associated members manually by selecting otherprofiles and indicating an association that can be recorded in themember's profile. Also, an association between two profiles may comprisean association automatically generated in response to a predeterminednumber of common entries, aspects, or elements in the two members'profiles. in one embodiment, a member profile may be associated with allof the other member profiles comprising a predetermined number orpercentage of common entries, such as interests, hobbies, likes,dislikes, employers and/or habits.

Associations between profiles within a member network can be of a singletype or can be multiple types and can include, for example, friendshipassociations, business associations, family associations, communityassociations, school associations, or any other suitable type of linkbetween profiles. Associations can further be weighted to represent thestrength of the association. For example, a friendship association canbe weighted more than a school association. Each type of association canhave various levels with different weights associated with each level.For example, a friendship association can be classified according towhich of a plurality of friendship association levels it belongs to. Inone embodiment, a friendship association may be assigned a level by themember from a list of levels comprising: a best friend, a good friend, aregular friend, an acquaintance, and a friend the member has not met.

In FIG. 2, the edges 218, 220, 222, 224, 226, 228, 230, 232, and 234shown comprise associations between profiles. According to theembodiment shown in FIG. 2, the member network 200 comprises a pluralityof differing types of associations represented by edges 218, 220, 222,224, 226, 228, 230, 232, and 234. The types of associations shown inFIG. 2 for illustration purposes are business associations, activitypartner associations, friendship associations, community associations,and common characteristic associations. Common characteristicassociations may include, for example, associations based on somecharacteristic, such as attending the same high school or being from thesame hometown, and can indicate a lower level of significance thananother type of association, such as a friendship association.

Referring to FIG. 2, edge 220 and edge 222 each comprise an associationbetween profile A at vertex 202 and profile D at vertex 208. The edge220 represents a business association, and the edge 222 represents afriendship association. Profile A is also associated with profile E by acommon characteristic association comprising edge 218. The associationbetween profile A and profile E may be more attenuated than theassociation between profile A and D, but the association can still berepresented by the member network depicted in FIG. 2.

Each member represented by the profiles A, B, C, D, E, F, and Gcomprising the vertices 202, 204, 206, 208, 210, 212, and 214,respectively, for purposes of illustration, comprises a person. Othertypes of members can be in member network 200. For example, communities,special interest groups, organizations, political parties, universities,and legal persons, such as corporations and business partnerships may bemembers of the member network 200. The associations 218, 220, 222, 224,226, 228, 230, 232, and 234 illustrated in FIG. 2 comprisebi-directional associations. An association between two profiles maycomprise a bi-directional association when both parties to theassociation are associated with each other. For example, in FIG. 2,profile A is associated with profile D, and profile D is also associatedwith profile A. In one embodiment, profiles A and D will not bebi-directionally associated with each other until both profiles consentto such an association. For example, profile A may invite profile D tobe associated therewith, and the bi-directional association occurs uponprofile D's acceptance of such invitation. The invitation, for example,may include sending an email or other message to profile D indicatingthat profile A has requested an association with profile D.

Other embodiments of the present invention may comprise directedassociations or other types of associations. Directed associations canassociate a first profile with a second profile while not requiring thesecond profile to be associated with the first profile. For example,profile A can be associated by a friendship association with profile B,and profile B can be unassociated with profile A, or profile B can beassociated with profile A through a different type of association, suchas a business association. Thus a display of profile A's friends wouldinclude profile B, but a display of profile B's friends would notinclude profile A.

Within a member network, a degree of separation can be determined forassociated profiles. In one embodiment, a degree of separation betweentwo profiles can be determined by the fewest number of edges of acertain type separating the associated profiles. In another embodiment,a type-specific degree of separation may be determined. A type-specificdegree of separation comprises a degree of separation determined basedon one particular type of association. For example, a profile A has afriend association degree of separation of two from profile E. Thefewest number of friendship associations between profile A and profile Eis two—the friendship association comprising edge 222 between profiles Aand D and the friendship association comprising edge 234 betweenprofiles D and E. Thus, for the associated profiles A and E, the degreeof friendship separation, determined according to one aspect of oneembodiment of the present invention, is two.

Another type-specific degree of separation can be determined forprofiles A and E. For example, a common characteristic degree ofseparation can be determined by determining the fewest number of commoncharacteristic associations separating profile A and profile E.According to the embodiment depicted in FIG. 2, there is one commoncharacteristic association, comprising edge 218, separating profiles Aand E. Thus, the co on characteristic association degree of separation,according to the embodiment depicted in FIG. 2, is one. The commoncharacteristic in this example, can be that profile A attended the samehigh school as profile E. A common characteristic association may beselected by profiles A and E to represent that they are associated insome fashion, but to not create a close association such as with afriendship association.

According to other aspects of certain embodiments of the presentinvention, the degree of separation may be determined by use of aweighting factor assigned to each association. For example, closefriendships can be weighted higher than more distant friendships.According to certain aspects of embodiments using a weighting factor, ahigher weighting factor for an association can reduce the degree ofseparation between profiles and lower weighting factors can increase thedegree of separation. This can be accomplished, for example, byestablishing an inverse relationship between each associations and acorresponding weighting factor prior to summing the associations. Thus,highly weighted associations would contribute less to the resulting sumthat lower weighted associations. Process

Various methods or processes in accordance with the present inventionmay be constructed. For example, in one embodiment, the method beginswith receiving profiles of users in a member network, wherein theprofiles comprise endorsement information of local articles and/or ads.The endorsement information can include a look-up table listing acategory classifying local search queries, an article identifier for anendorsed local article or ad in the category, and an endorsementassociated with the article identifier. The endorsements come frommembers of the member network, and they can be binary endorsements ofthe local articles and/or ads, ratings of the local articles and/or ads,and/or comments about the local articles and/or ads. Financialincentives can be provided to endorsing users. The method also includesreceiving a local search query from a user within or outside of themember network and providing local articles relevant to the local searchquery, wherein one of the local articles may be endorsed based on theendorsement information. When the received local search query isclassified in the same category found in the endorsement information,the provided local article for the received local search query is theendorsed local article listed in the endorsement information. Theendorsed local articles and/or ads are from members associated with theuser that submits the received local search query. The associations canbe based on profile associations indicated in user profiles and/or thereceived local search query.

FIGS. 3A-B illustrate an exemplary method that provides local searchendorsements whereby members can create and share their personalizedlists of local articles and/or ads with other members in the membernetwork and/or other users of a search engine. The exemplary method isprovided by way of example, as there are a variety of ways to carry outmethods according to the present invention. The method shown in FIGS.3A-B can be executed or otherwise performed by one or a combination ofvarious systems. The method in FIGS. 3A-B is described below as carriedout by the system 100 shown in FIG. 1 by way of example, and variouselements of the system 100 are referenced in explaining the examplemethod of FIGS. 3A-B.

The method begins at 310 with a user, such as user 112 a, joining amember network, such as the member network 200 (e.g., Orkut™), bycreating a user or member profile as described earlier.

At 320, the user 112 a submits a local search query to a search engine,such as the search engine 158 at the search site 152, that is linked tothe member network site 160. The local search query includes one or moreitems to be searched (e.g., a sushi restaurant) and a particularlocality for the search (e.g., San Francisco). The user 112 a submitsthe local search query by generating a query signal that includes thelocal search query at the client device 102 a and transmitting the querysignal to the server device 152 via the network 106.

At 330, in response to receiving the query signal from the client device102 a, the search engine 158 locates the desired local information in amanner know in the art and return article identifiers representing thedesired local information in a local search result set. The searchengine 158 further categorizes the local search query. For instance,referring to the example of the local search query for a sushirestaurant in San Francisco, such query can be categorized under“Japanese or Sushi Restaurants”, and listed under a locality subcategoryof “San Francisco”. Alternatively, such query can be categorized underthe San Francisco locality and listed under a subcategory for “Japaneseor Sushi Restaurants”. Thus, the types of categorization used for localsearch queries merely depend on the desire of the search site 150.

Each article identifier in the local search result set returned by thesearch engine 158 is provided with one or more “endorse” links thatenables the user 112 a to endorse or recommend the underlying localarticle represented by the article identifier for the particular issuedsearch query. The endorsement/recommendation can be a simple binaryendorsement (e.g., a positive or negative endorsement) of the localarticle, a scaling system (e.g., 1 to 5 or A to F to indicate best toworst) rating the local article, and/or other added comments about thelocal article.

At 340, the user 112 a can endorse a local article by accessing therespective “endorse” link(s). For instance, the user 112 a can click onthe “endorse” link and be directed to a navigational page or window,wherein the user 112 a is presented with the option to either positivelyendorse (e.g., “Recommended”) or negatively endorse (e.g., “NotRecommended”) the local article. In another instance, the user 112 a canclick on the “endorse” link and be directed to a navigational page orwindow, wherein the user 112 a is presented with a scale, e.g., from 1to 5 to rate the local article from best to worse or vice versa. Instill another instance, separately or in combination with theaforementioned two instances, the user 112 a can click on the “endorse”link and be directed to a navigational page or window, wherein the user112 a has the option to add comments about his/her opinion of the localarticle.

Alternatively, the user 112 a can endorse a local article without havingto first obtain the local article from a search query. For instance, theuser 112 a can create a search endorsement entry in his or her memberprofile, wherein the user 112 a can input a desired search query for theendorsement, one or more article identifiers of local articles that theuser 112 a chooses to endorse for the desired search query, and the kindof endorsement for each of the endorsed local article identifiers.

In a further instance, the user 112 a does not click on the available“endorse” link but click on the actual local article identifier tonavigate to the actual local article. The search engine 158 then notessuch access and transmits that information to the member network site160 for storage in the profile of the user 112 a in the member networkdatabase 170. At some point in the future, when the user 112 a accessesthe search engine 158 for another search, and the search engine 158,communicating with the member network engine 168, links the user 112 awith his/her member profile in the member network database 170, thesearch engine 158 can then prompt the user 112 a to endorse theparticular local article that the user 112 a previously accessed but didnot endorse. The user 112 a can receive such prompt for endorsement thenext time he/she accesses the search engine 158 or after a predeterminednumber of accesses to the search engine 158. Also, the user 112 a may beprompted one or more times as desired. If the user 112 a chooses todisregard the prompts, the search engine 158 can stop prompting the user112 a to endorse such local article after a predetermined number ofprompts. Subsequently, the search engine 158 can bring up anotherarticle identifier for a local article that the user 112 a previouslyaccessed but did not endorse and repeat the above prompt process again.

At 345, the user 112 a can also endorse any number of ads for aparticular local search query in addition to or in lieu of the localarticles. As referred herein, an ad can be an online ad, such as abanner ad, a pop-up window ad, or simply a listing of a name of aproduct/service provider along with contact information. An ad can alsobe in any format presently known or prospectively contemplated in theart and accessible via its article identifier by the search engine 158and member network engine 168. According to one embodiment of thepresent invention, the act of endorsing an ad is separate from the actof accessing the search engine 158 to perform a search as describedabove. Also, the endorsed ad may or may not contain the same informationas one of the local articles that the search engine 158 can come up withfor a particular local search query. For instance, prior or subsequentto accessing the search engine 158 for a search, the user 112 a may haveendorsed an ad through a website that handles such ad. The ad websitecan then forwards information about the endorsed ad to the membernetwork site 160 so that the member network engine 168 can store theendorsed ad along with the proper local category for the ad in theprofile of the user 112 a in the member network database 170. Thecategorization of endorsed ads is similar to that for local articles asdescribed earlier.

At 350, once the user 112 a has endorsed one or more local articles asrepresented by their article identifiers in the local search result setand/or the user 112 a has endorsed one or more ads as also representedby their article identifiers, the search engine 158 can communicate withthe member network engine 168 to store the endorsements in the memberprofile in the member network database 170. The endorsements arecategorically stored in accordance with the category in which the localsearch query submitted by the user 112 a is listed.

Accordingly, the member profile of the user 112 a can include at leastthe following information: 1) an identifier of the user 112 a (who canbe anonymous); 2) a list of other users in the member network 200 thathave one or more associations with the user 112 a as described earlier;3) a list of categories of local search queries that have local searchendorsements; 4) a list of endorsed local articles (e.g., names of aproduct/service providers and their contact information) and/or ads ineach of the categories previously submitted by the user 112 a; and/or 5)the endorsements by the user 112 a for each of the endorsed localarticles and/or ads. Alternatively, the endorsements can include theaforementioned information but be stored in a file other than the memberprofile and yet be associated with the member.

Referring now to FIG. 3B, at 360, another user, such as user 112 b,submits a local search query to the same search site 150. As describedearlier with regard to 320, the user 112 b submits the local searchquery at 370 by generating a query signal that includes a local searchquery at the client device 102 b and transmitting the query signal tothe server device 152 via the network 106.

In response to receiving the query signal from the client device 102 bat 370, the search engine 158 performs two searches: 1) at 371, aregular local search similar to 330 in FIG. 3A whereby the search engine158 locates the desired local information in a manner know in the artand return article identifiers associated with the local information ina first local search result set; and 2) at 372, a search of the membernetwork database 170 whereby the search engine 158 locates previouslyendorsed local articles and/or ads in the same category or categories asthe local search query submitted by the user 112 b in associated memberprofiles in a member network and return them in a second local searchresult set. The search engine 158 searches the member network database170 by first communicating with the server device 162 and its membernetwork engine 168 to identify members in the member network 200 thatare associated with the user 112 b and/or associated with the localsearch query submitted by the user 112 b. Next, through the membernetwork engine 168, the search engine 158 can access the member profilesof those associated members to look up any available list of endorsedlocal articles and/or ads (via their article identifiers) in theappropriate one or more categories as described.

Thus, the user 112 b can add a layer of trust on top of the regularlocal search result set. The trust can be in the form of local searchendorsements from those members of the member network 200 that areassociated with the user 112 b because of their explicit profileassociations with the user 112 b, as described earlier with reference toFIG. 3. The trust can also be in the form of local search endorsementsfrom those members that are associated with the user 112 b because oftheir implicit profile associations with the user 112 b and/or the localsearch query submitted by the user 112 b. In one implicit profileassociation, the search engine 158 and member network engine 168 canidentify from member profiles stored in the member network database 170those members that have the same or similar interests with the user 112b, based on a comparison between the profile of the user 112 b andprofiles of other users in the member network 200 or a comparisonbetween the profiles of other users in the member network 200 and thelocal search query submitted by the user 112 b. The search engine 158and member network engine 168 can then provide the user 112 b with anylocal article and/or ad endorsements that are classified in the samecategory or categories with the local search query submitted by the user112 b from such implicitly-associated members. For instance, if the user112 b is searching for a sushi restaurant in San Francisco, the user 112b can receive endorsements from those members that live in the SanFrancisco area and like Japanese or sushi restaurant or from thosemembers that are food critics in the San Francisco area. To provideanother example, if both the users 112 a and 112 b are members of a“photography” community within the member network 200, and the user 112b is searching for a digital camera, the user 112 b can receive anyendorsements from the user 112 a and other members of the “photography”community on the kind of digital camera to purchase and/or where topurchase a digital camera. From the present disclosure, one of ordinaryskill in the art can see that there are a myriad of criteria that can beused to identify those members that can be implicitly associated withthe user 112 b. Those criteria merely depend on the extent of theinformation contained in the member profiles for the member network 200and the local search queries entered by the user 112 b to obtainendorsed local articles and/or ads.

At 380, the search engine 158 then merges the first and second localsearch result sets together to form a final local search result set.According to one embodiment of the present invention, any endorsed localarticles and/or ads in the final search result set can be rankeddifferently from other local articles in the set and annotated toindicate endorsements. The ranking for each endorsed local articleand/or ad can be based on the type and/or degree of associations(implicit or explicit) between the user 112 b and the member in themember network 200 that has endorsed such local article and/or ad. Forinstance, the final search result set is the regular first local searchresult set that has been re-ordered or re-ranked with articleidentifiers for the endorsed local articles and/or ads (found from thesecond local search result set) arranged at the top of the list withannotations to indicate endorsements. The user 112 b can then access theannotations to view all endorsements for each endorsed local articleand/or ad and identify the user or users that made the endorsements.This allows the user 112 b to appropriately trust the endorsements basedon his/her associations (implicit or explicit) with the endorsers. Theannotations (e.g., “endorsed by John Doe” and/or “from John Doe: a greatplace for buying widgets”) can accompany the article identifier of anendorsed local article or ad and be placed in the vicinity of thearticle identifier. Alternatively, the annotations can be links topop-up windows or other sites/pages that contain the endorsementlanguage. According to another embodiment of the present invention, theuser 112 b can be automatically redirected to the navigational site ofan endorsed article or ad based on the ranking or re-ordering.

Although the aforementioned embodiments of the present invention havebeen described with regard to query-dependent endorsements, i.e.,endorsed search results are provided based on search queries submittedby a user, it should be understood that query-independent endorsementsalso can be provided. FIGS. 6A-B illustrate an exemplary method thatprovides query-independent article endorsements whereby members cancreate and share their personalized lists of articles with other membersin the member network and/or other users of a search engine. Theexemplary method is provided by way of example, as there are a varietyof ways to carry out methods according to the present invention. Themethod shown in FIGS. 6A-B can be executed or otherwise performed by oneor a combination of various systems. The method in FIGS. 6A-B isdescribed below as carried out by the system 100 shown in FIG. 1 by wayof example, and various elements of the system 100 are referenced inexplaining the example method of FIGS. 6A-B.

The method begins at 610 and continues at 620 and 630 in a similarmanner as described earlier for 310, 320, and 330 in FIG. 3A,respectively.

At 640, similar to 340 in FIG. 3A, the user 112 a can endorse a localarticle by accessing the respective “endorse” link(s). For instance, theuser 112 a can click on the “endorse” link and be directed to anavigational page or window, wherein the user 112 a is presented withthe option to either positively endorse (e.g., “Recommended”) ornegatively endorse (e.g., “Not Recommended”) the local article. Inanother instance, the user 112 a can click on the “endorse” link and bedirected to a navigational page or window, wherein the user 112 a ispresented with a scale, e.g., from 1 to 5 to rate the local article frombest to worse or vice versa. In still another instance, separately or incombination with the aforementioned two instances, the user 112 a canclick on the “endorse” link and be directed to a navigational page orwindow, wherein the user 112 a has the option to add comments abouthis/her opinion of the local article.

Alternatively, the user 112 a can endorse a local article without havingto first obtain the local article from a search query. For instance, theuser 112 a can create a search endorsement entry in his or her memberprofile, wherein, unlike 340 in FIG. 3A, the user 112 a does not have toinput a desired search query for the endorsement (because the endorsedsearch result set will be query-independent anyway, although the user112 a can still input a desired search query). Here, the user 112 a alsocan input one or more article identifiers of local articles that theuser 112 a chooses to endorse, and the kind of endorsement for each ofthe endorsed local article identifiers.

In a further instance, the user 112 a does not click on the available“endorse” link but click on the actual local article identifier onavigate to the actual local article. The search engine 158 then notessuch access and transmits that information to the member network site160 for storage in the profile of the user 112 a in the member networkdatabase 170. At some point in the future, when the user 112 a accessesthe search engine 158 for another search, and the search engine 158,communicating with the member network engine 168, links the user 112 awith his/her member profile in the member network database 170, thesearch engine 158 can then prompt the user 112 a to endorse theparticular local article that the user 112 a previously accessed but didnot endorse. The user 112 a can receive such prompt for endorsement thenext time he/she accesses the search engine 158 or after a predeterminednumber of accesses to the search engine 158, Also, the user 112 a may beprompted one or more times as desired. If the user 112 a chooses todisregard the prompts, the search engine 158 can stop prompting the user112 a to endorse such local article after a predetermined number ofprompts. Subsequently, the search engine 158 can bring up anotherarticle identifier for a local article that the user 112 a previouslyaccessed but did not endorse and repeat the above prompt process again.

At 645, similar to 345 in FIG. 3, the user 112 a can also endorse anynumber of ads in addition to or in lieu of the local articles. Asreferred herein, an ad can be an online ad, such as a banner ad, apop-up window ad, or simply a listing of a name of a product/serviceprovider along with contact information. An ad can also be in any formatpresently known or prospectively contemplated in the art and accessiblevia its article identifier by the search engine 158 and member networkengine 168. According to one embodiment of the present invention, theact of endorsing an ad is separate from the act of accessing the searchengine 158 to perform a search as described above. Also, the endorsed admay or may not contain the same information as one of the local articlesthat the search engine 158 can come up with for a particular localsearch query. For instance, prior or subsequent to accessing the searchengine 158 for a search, the user 112 a may have endorsed an ad througha web site that handles such ad. The ad web site can then forwardsinformation about the endorsed ad to the member network site 160 so thatthe member network engine 168 can store the endorsed ad along with theproper local category for the ad in the profile of the user 112 a in themember network database 170. The categorization of endorsed ads issimilar to that for local articles as described earlier.

At 650, similar to 350 in FIG. 3A, once the user 112 a has endorsed oneor more local articles as represented by their article identifiers inthe local search result set and/or the user 112 a has endorsed one ormore ads as also represented by their article identifiers, the searchengine 158 can communicate with the member network engine 168 to storethe endorsements in the member profile in the member network database170. The endorsements are categorically stored in accordance with thecategory in which the local search query submitted by the user 112 a islisted.

Accordingly, the member profile of the user 112 a can include at leastthe following information: 1) an identifier of the user 112 a (who canbe anonymous); 2) a list of other users in the member network 200 thathave one or more associations with the user 112 a as described earlier;3) a list of local categories that have local search endorsements; 4) alist of endorsed local articles (e.g., names of a product/serviceproviders and their contact information) and/or ads in each of thecategories previously submitted by the user 112 a; and/or 5) theendorsements by the user 112 a for each of the endorsed local articlesand/or ads. Alternatively, the endorsements can include theaforementioned information but be stored in a file other than the memberprofile and yet be associated with the member.

Referring now to FIG. 6B, the method continues at 660, which is similarto 360 in FIG. 3 and described earlier. Next, in response to receivingthe query signal from the client device 102 b at 370, the search engine158 performs two searches: 1) at 371, a regular local search similar to330 in FIG. 3A whereby the search engine 158 locates the desired localinformation in a manner know in the art and return article identifiersassociated with the local information in a first local search resultset; and 2) at 372, a search of the member network database 170 wherebythe search engine 158 locates previously endorsed local articles and/orads in associated member profiles in a member network and return them ina second local search result set. The search engine 158 searches themember network database 170 by first communicating with the serverdevice 162 and its member network engine 168 to identify members in themember network 200 that are associated with the user 112 b. Next,through the member network engine 168, the search engine 158 can accessthe member profiles of those associated members to look up any availablelist of endorsed local articles and/or ads (via their articleidentifiers) in the appropriate one or more categories as described forthose articles that match with the article identifiers contained in thefirst search result set.

Thus, the user 112 b can add a layer of trust on top of the regularlocal search result set, even though such layer of trust may bequery-independent. The trust can be in the form of local searchendorsements from those members of the member network 200 that areassociated with the user 112 b because of their explicit profileassociations with the user 112 b, as described earlier with reference toFIG. 2. The trust can also be in the form of local search endorsementsfrom those members that are associated with the user 112 b because oftheir implicit profile associations with the user 112 b. In one implicitprofile association, the search engine 158 and member network engine 168can identify from member profiles stored in the member network database170 those members that have the same or similar interests with the user112 b, based on a comparison between the profile of the user 112 b andprofiles of other users in the member network 200 or a comparisonbetween the profiles of other users in the member network 200 and thelocal search query submitted by the user 112 b. The search engine 158and member network engine 168 can then provide the user 112 b with anylocal article and/or ad endorsements that are classified in the samecategory or categories with the local search query submitted by the user112 b from such implicitly-associated members.

To provide an example, if the user 112 b is searching for a sushirestaurant in San Francisco, the user 112 b can receive endorsementsfrom those members that live in the San Francisco area and like Japaneseor sushi restaurant or from those members that are food critics in theSan Francisco area, even though such endorsements contained in themember profiles of those members may not have associated any assignedsearch queries at all but merely match some of the search resultsinitially returned by the search engine 158. To provide another example,if both the users 112 a and 112 b are members of a “photography”community within the member network 200, and the user 112 b is searchingfor a digital camera, the user 112 b can receive any endorsements fromthe user 112 a and other members of the “photography” community on thekind of digital camera to purchase and/or where to purchase a digitalcamera. From the present disclosure, one of ordinary skill in the artcan see that there are a myriad of criteria that can be used to identifythose members that can be implicitly associated with the user 112 b.Those criteria merely depend on the extent of the information containedin the member profiles for the member network 200 as entered by the user112 b to obtain endorsed local articles and/or ads.

At 680, the method continues as described earlier for 380 of FIG. 3.

FIGS. 3A and 6A have been described with reference to one user, namely.user 112 a, and FIGS. 3B and 6B have been described with reference toanother user, namely, user 112 b. However, it should be noted that aplurality of users, including the user 112 a or aside from the user 112a, may have endorsed a plurality of local articles and/or ads in thesame category or categories of the local search query subsequentlysubmitted by the user 112 b. Also, the users 112 a and 112 b can be oneand the same. Hence, according to one embodiment of the presentinvention, each article identifier in the final search result setdescribed in FIGS. 3B and 6B, regardless of its endorsed or non-endorsedstatus, can be provided with one or more “endorse” links that enablesthe user 112 b to also endorse and/or recommend the underlying localarticle or ad for the submitted search query, just as the user 112 a isable to do. Further, the user 112 b can still benefit from local searchendorsements by members of the member network 200 even when the user 112b is not in a member network 200. This is because, as mentioned earlier,the search engine 158 and the member network engine 168 can retrieveendorsements from stored member profiles in the member network database170 that are implicitly associated with the user 112 b based on just thelocal search query submitted by the user 112 b.

Consequently, the local search endorsements can be used to personalizethe search engine's ranking of article identifiers in a search resultset by offering a way for users to re-rank the article identifiers forthemselves and for those associated with them in the member network 200.

According to one embodiment of the present invention, the local searchendorsements can provide financial opportunities to both the users whoendorse local articles and/or ads and the search site that implementsthe local search endorsements. For example, to encourage users toendorse a particular article or ad for a particular category, such website can offer to pay each endorsing user a financial proceed (e.g., afee for each click through) for the endorsement. The search site canalso retain a portion of such proceed.

EXAMPLE

Referring to the screenshot shown in FIG. 4, with reference to FIGS. 1and 2, an example in accordance with an embodiment of the presentinvention is now provided. When a user A (who can be, e.g., any one ofthe users 112 a-n), having a profile A at vertex 202 in FIG. 2 submits,a search query for, e.g., widgets in Chicago, to the search engine 158at the search site 150, the search engine returns a search result set asshown in FIG. 4 to the user A. As shown, the article identifier 410indicates an endorsement of the underlying article with a comment by auser D (having profile D at vertex 208 in FIG. 2). The articleidentifier 520 indicates a negative endorsement by a user E (havingprofile E at vertex 410 in FIG. 3) with no comments. The articleidentifier 430 indicates endorsements by users B (having profile B atvertex 204 in FIG. 2) and C (having profile C at vertex 206 in FIG. 2)with comments only from C. The article identifier 440 indicates anendorsement by a user F (having profile F at vertex 212 in FIG. 2) withno comments. The article identifier 450 indicates no endorsements orcomments.

From viewing the search results, the user A can decide to: 1) trust thearticles represented by article identifiers 410 and 430 because they areendorsed by his/her friends, user B, C, and D (as shown by thefriendship associations 226, 32, and 222, respectively); 2) trust thearticle represented by article identifier 420 a little less because itis endorsed by his/her friend of a friend, user E; and/or 3) not trustthe articles represented by article identifier 440 or 450 becausearticle identifier 440 is endorsed by a user F with whom the user A isstrenuously associated (a friend F of a friend G of a friend C) andarticle identifier 450 is not endorsed by anyone. Alternatively, if theuser F is implicitly associated with the user A (e.g., the user F is aWidget expert or aficionado in the Chicago area based on his/her userprofile, or both users A and F are members of widget community orassociation within the member network 200), the user A can choose totrust the article represented by article identifier 440. The user A canalso trust the article represented by article identifier 430 more thanthe article represented by article identifier 410 because the user A isassociated in more ways with the user B than with the user D (threeassociations 224, 226, 228 versus two associations 220 and 222, as seenin FIG. 2).

As mentioned earlier, the user A also has an opportunity to provideendorsements and/or comments to one or more of the articles representedby article identifiers 410, 420, 430, 440, and 450 by accessing the link401 that accompanies each of the article identifiers.

FIG. 5 shows a sample screenshot 500 of an endorsement page that isprovided when the user A accesses the link 401 of the article identifier410, in accordance with an embodiment of the present invention. Asshown, the user A can positively endorse the link by clicking on button510, negatively endorse the link by clicking on button 520, and/or addcomments in box 530. Once finished, the user A can click on button 540to end the endorsement process, store the endorsement in his/her userprofile in the member network database 170, and return to the searchresult page shown in FIG. 4. The endorsement by user A will then beadded to a search result page in a similar manner to that shown in FIG.4.

General

Although the invention has been described with reference to theseembodiments, other embodiments could be made by those in the art toachieve the same or similar results. Variations and modifications of thepresent invention will be apparent to one skilled in the art based onthe present disclosure, and the present invention encompasses all suchmodifications and equivalents.

1. A computer-implemented method comprising: receiving a first searchquery from a first member of a member network; obtaining a firstresponse to the first search query, wherein the first responseidentifies a plurality of electronic articles that were each rated by atleast a respective one member of the member network; for each electronicarticle of the plurality of electronic articles, obtaining a respectiverating provided by each of one or more members of the member network,for each rating, applying an adjustment to generate an updated rating,wherein the adjustment is based on a credibility factor associated withthe member who provided the rating or a degree of separation between thefirst member and the member who provided the rating, and combining theone or more updated ratings to generate a combined member-based ratingand using the combined member-based rating to generate a ranking scorefor the electronic article; and using the ranking scores of theplurality of electronic articles to generate a ranking of the electronicarticles; and providing, for display to the first member, in a responseto the first search query, search results representing highest rankingelectronic articles in the ranking of the electronic articles.
 2. Themethod of claim 1, wherein: the adjustment is based on the credibilityfactor associated with the member who provided the rating.
 3. The methodof claim 2, wherein: the adjustment is also based on the degree ofseparation between the first member and the member who provided therating.
 4. The method of claim 1, wherein: the adjustment is based onthe degree of separation between the first member and the member whoprovided the rating.
 5. The method of claim 1, wherein: the degree ofseparation between the first member and the member who provided therating is a lowest count of eligible associations separating the firstmember and the member who provided the rating, wherein an association iseligible if a type of the association is an eligible association type.6. The method of claim 5, wherein a friendship association type is aneligible association type.
 7. The method of claim 5, wherein a businessassociation type is not an eligible association type.
 8. The method ofclaim 5, wherein an implicit association based on geographic proximityis an eligible association type.
 9. The method of claim 5, wherein animplicit association based on having at least a threshold number ofcommon characteristics is an eligible association type.
 10. The methodof claim 1, further comprising: determining one or more groups ofeligible associations that each separate the first member and the memberwho provided the rating, wherein: an association is eligible if a typeof the association is an eligible association type, each group ofeligible associations includes one or more associations, and eachassociation of the one or more associations in each group has a weight;for each group of the one or more groups of eligible associations,aggregating the weight of each eligible association in the group togenerate a cumulative weight for the group; and determining thecumulative weight for a group in the one or more groups of eligibleassociations having a lowest cumulative weight as the degree ofseparation between the first member and the member who provided therating.
 11. The method of claim 10, wherein the weight for each eligibleassociation is determined based on the type of the eligible association.12. A system comprising one or more computers and one or more storagedevices storing instructions that are operable, when executed by the oneor more computers, to cause the one or more computers to performoperations comprising: receiving a first search query from a firstmember of a member network; obtaining a first response to the firstsearch query, wherein the first response identifies a plurality ofelectronic articles that were each rated by at least a respective onemember of the member network; for each electronic article of theplurality of electronic articles, obtaining a respective rating providedby each of one or more members of the member network, for each rating,applying an adjustment to generate an updated rating, wherein theadjustment is based on a credibility factor associated with the memberwho provided the rating or a degree of separation between the firstmember and the member who provided the rating, and combining the one ormore updated ratings to generate a combined member-based rating andusing the combined member-based rating to generate a ranking score forthe electronic article; and using the ranking scores of the plurality ofelectronic articles to generate a ranking of the electronic articles;and providing, for display to the first member, in a response to thefirst search query, search results representing highest rankingelectronic articles in the ranking of the electronic articles.
 13. Thesystem of claim 12, wherein: the adjustment is based on the credibilityfactor associated with the member who provided the rating.
 14. Thesystem of claim 13, wherein: the adjustment is also based on the degreeof separation between the first member and the member who provided therating.
 15. The system of claim 12, wherein: the adjustment is based onthe degree of separation between the first member and the member whoprovided the rating.
 16. The system of claim 12, wherein: the degree ofseparation between the first member and the member who provided therating is a lowest count of eligible associations separating the firstmember and the member who provided the rating, wherein an association iseligible if a type of the association is an eligible association type.17. The system of claim 16, wherein an implicit association based ongeographic proximity is an eligible association type.
 18. The system ofclaim 16, wherein an implicit association based on having at least athreshold number of common characteristics is an eligible associationtype.
 19. The system of claim 12, further comprising: determining one ormore groups of eligible associations that each separate the first memberand the member who provided the rating, wherein: an association iseligible if a type of the association is an eligible association type,each group of eligible associations includes one or more associations,and each association of the one or more associations in each group has aweight; for each group of the one or more groups of eligibleassociations, aggregating the weight of each eligible association in thegroup to generate a cumulative weight for the group; and determining thecumulative weight for a group in the one or more groups of eligibleassociations having a lowest cumulative weight as the degree ofseparation between the first member and the member who provided therating.
 20. A computer storage medium encoded with instructions that,when executed by one or more computers, cause the one or more computersto perform operations comprising: receiving a first search query from afirst member of a member network; obtaining a first response to thefirst search query, wherein the first response identifies a plurality ofelectronic articles that were each rated by at least a respective onemember of the member network; for each electronic article of theplurality of electronic articles, obtaining a respective rating providedby each of one or more members of the member network, for each rating,applying an adjustment to generate an updated rating, wherein theadjustment is based on a credibility factor associated with the memberwho provided the rating or a degree of separation between the firstmember and the member who provided the rating, and combining the one ormore updated ratings to generate a combined member-based rating andusing the combined member-based rating to generate a ranking score forthe electronic article; and using the ranking scores of the plurality ofelectronic articles to generate a ranking of the electronic articles;and providing, for display to the first member, in a response to thefirst search query, search results representing highest rankingelectronic articles in the ranking of the electronic articles.